A Deep Learning Approach Based on Stacked Denoising Autoencoders for Protein Function Prediction

Abstract

Predicting protein functions is a fundamental task with applications in medicine and healthcare. However, the accelerating pace of protein-discovery renders slow and expensive biochemical techniques unsustainable. Machine learning is suitable for such data-intensive task, but the presence of noise in protein datasets adds another level of difficulty. Hence, we propose a deep learning system based on a stacked denoising autoencoder that extracts robust features to improve predictive performance. We then feed the resulting features to a multilabel support-vector machine for classification. We evaluated on two protein benchmarks, and experimental results show that our system consistently produced the best performance against techniques that do not have a denoising or feature learning capability. This research demonstrates that learning robust representations from raw data can benefit the process of predicting protein functions.